About MCP Telemetry
MCP Telemetry is an MCP server for recording and analyzing conversations between users and LLMs. It is intended for chat system operators who need conversation traces, with Weights & Biases Weave for visualization and analysis.
Key features
- 01Start sessions with custom identifiers
- 02Record inputs, responses, actions, and tool results
- 03Monitor conversation flows in real time
- 04Export and share conversation analytics
- 05Visualize traces with Weights & Biases Weave
Use cases
- 01Trace a Claude conversation for debugging
- 02Review tool usage and conversation flows
- 03Inspect response times and error rates
MCP support
The MCP server exposes telemetry tools to MCP clients for tracking and analyzing conversations, including user inputs, LLM responses, and tool calls.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEMCP Telemetry
A Model Context Protocol (MCP) server for telemetry within chat systems using Weights & Biases Weave
Overview
MCP Telemetry provides a simple interface for logging and tracking conversations between users and LLMs. It leverages the Model Context Protocol to expose telemetry tools that can be used to trace and analyze conversations.
Features
- Start tracing sessions with custom identifiers
- Log comprehensive conversation data including:
- User inputs
- LLM responses
- LLM actions
- Tool calls and their results
- Seamless integration with Weights & Biases Weave for visualization and analysis
- Real-time monitoring of conversation flows
- Export and share conversation analytics
Installation
First, get a WandB API Key from: https://wandb.ai/settings#api
This server can be installed by adding the following json to your Claude desktop config:
{
"mcpServers": {
"MCP Telemetry": {
"command": "uv", -- this needs to be the location where uv is available, check via 'which uv'
"args": [
"run",
"--with",
"mcp[cli]",
"--with",
"weave",
"mcp",
"run",
"~/mcp-telemetry/server.py"
],
"env": {
"WANDB_API_KEY": "..." -- get one from wandb.com
}
}
}
}
Usage
Once installed, the MCP Telemetry server will automatically start when you launch Claude. It will begin collecting telemetry data for all conversations. You can view your telemetry data in the Weights & Biases dashboard.
Basic Usage
- Start a conversation with Claude
- The server will automatically track:
- User messages
- LLM responses
- Tool calls and their results
- Conversation metadata
Configuration
The server can be configured through environment variables:
WANDB_API_KEY- Your Weights & Biases API key (required)
Examples
Starting a Tracing Session
Prompt Claude to trace that conversation. Example: Log this conversation with MCP Telemetry, topic will be Cats
Viewing Telemetry Data
- Log in to your Weights & Biases account
- Navigate to your project
- You'll see various visualizations including:
- Conversation flows
- Tool usage patterns
- Response times
- Error rates
Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
License
This project is licensed under the MIT License - see the LICENSE file for details.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.